Mountain photovoltaic power station terrain modeling and optimizing method and device and storage medium

Abstract

The invention provides a mountain photovoltaic power station terrain modeling and optimizing method and device and a storage medium, and the method comprises the following steps: 1, filtering initial data, and removing redundant data and noise points; 2, constructing a triangular mesh, and generating an initial terrain surface; 3, optimizing the initial terrain surface by using a ray dot matrix sampling technology to obtain an updated terrain; and 4, analyzing the inclination angle and orientation of the terrain, and screening out an area suitable for photovoltaic array arrangement. The technology mainly utilized by the method comprises a ray method, a TIN algorithm and the like. By setting the interval density of the ray dot matrix, the total amount of mountain modeling can be reduced, and the precision degree of modeling can be locally improved, so that the problems of long modeling operation time, insufficient precision and the like are solved, and the photovoltaic module layout is more scientific and reasonable through accurate terrain modeling. The landform screening is carried out by analyzing the landform inclination angle and orientation, the generating capacity of the mountain photovoltaic power station can be maximized, the construction difficulty and cost are reduced, and complex landforms are avoided to simplify the construction and optimize the layout to improve the land utilization rate.

Description

Technical Field

[0001] The present invention relates to the technical field of terrain modeling, and in particular to a terrain modeling and optimization method, device and storage medium for mountain photovoltaic power stations. Background Art

[0002] With the development of large-scale and centralized photovoltaic power generation, high-quality plain areas are becoming increasingly scarce, and mountainous areas have gradually become one of the main options for photovoltaic power station development. In actual mountain photovoltaic power station systems, there are certain requirements for the layout of mountainous terrain. The azimuth and inclination of the terrain where the photovoltaic array is installed are mainly considered. Selecting terrain with a gentle orientation and slope for photovoltaic array placement can maximize the efficiency of the photovoltaic power station. Therefore, it is necessary to model and optimize the mountainous terrain and determine which areas of the entire three-dimensional terrain meet the requirements for photovoltaic array placement.

[0003] In the research of terrain modeling and related technology applications, there are currently a series of problems that need to be solved. These problems have, to a certain extent, restricted the further development of terrain modeling technology and its efficient application in practical engineering. The main problems are:

[0004] 1. Poor Visualization: Currently, terrain modeling is presented in a relatively limited format, primarily relying on traditional methods such as 2D contour lines and CAD drawings. While 2D contour lines can reflect terrain undulations to a certain extent, they lack intuitive and comprehensive representation of complex terrain details, making it difficult for users to quickly and accurately grasp its three-dimensional spatial structure. CAD drawings, widely used in engineering mapping, focus on precise line graphics when used for terrain modeling visualization. This lacks the realism and three-dimensionality of the terrain, failing to meet users' intuitive visual needs. This single visualization method limits the communication of terrain information.

[0005] 2. High modeling cost: Drone scanning technology is efficient and accurate, and can quickly obtain terrain information over large areas. However, it requires professional equipment, personnel, and high-level technical skills to process data, which is costly and has a limited scope of application.

[0006] 3. Lack of an integrated approach to data conversion: Terrain modeling involves multiple data formats and types, including point clouds, images, and vectors. Modeling requires integrating and processing data from various sources and formats. Due to the lack of unified standards, issues such as information loss and format incompatibility can easily arise, increasing the difficulty and workload of data processing and potentially impacting the accuracy and reliability of modeling results. For example, when converting point cloud data to a triangulated network model, imperfect algorithms can result in uneven surface quality and loss of detail, compromising the quality of terrain modeling.

[0007] 4. Difficulty adjusting terrain modeling precision: Existing terrain modeling methods rely too heavily on fixed processes and parameter settings, lacking practical flexibility. Current methods struggle to combine local refinement with more extensive modeling, and lack the flexibility to adjust precision as needed. This leads to either excessive resource waste or a reduction in overall accuracy to reduce costs, impacting the accuracy of subsequent analytical decisions. Summary of the Invention

[0008] This invention aims to address existing issues in the existing technology by providing a terrain modeling and optimization method, device, and storage medium for mountain photovoltaic power plants. By combining the ray method with a triangulated irregular network (TIN) algorithm, this method improves modeling efficiency, enhances computational stability, facilitates dynamic adjustments based on design requirements, improves visualization, and ensures plant performance.

[0009] The technical solutions of the present invention are as follows:

[0010] A method for terrain modeling and optimization of a mountain photovoltaic power station comprises the following steps:

[0011] 1. Filter the initial data to remove redundant data and noise points;

[0012] Initial data contains a vast number of sampling points, including both key terrain features and a significant amount of redundant data and noise. Redundant data increases the computational complexity of subsequent triangulation and reduces algorithm efficiency. Noise interferes with the accurate representation of terrain features and affects the accuracy of the terrain model. To improve the efficiency and accuracy of the TIN algorithm, this patent uses a 3D line synthesis method to filter the initial data for the TIN algorithm.

[0013] As an effective data simplification and feature extraction technique, the 3D line synthesis method is based on the core principle of the two-dimensional Douglas-Peucker algorithm, which is extended to three-dimensional space to handle complex terrain data. Referring to "Contour Line Synthesis Using the 3D Douglas-Peucker Algorithm" (Journal of Surveying and Mapping Science and Technology, 2009, 26(06):444-448), the 3D line synthesis method is used to filter the initial data of the TIN algorithm, accurately selecting points that play a key role in expressing terrain features. On the one hand, it can remove redundant points that have little impact on the overall terrain morphology, reduce the data volume, reduce the computational complexity of TIN construction, and improve the algorithm speed. On the other hand, it can filter out noise points to prevent them from interfering with the terrain model, making the constructed TIN model more accurately reflect the actual terrain characteristics and improving the accuracy and reliability of terrain modeling.

[0014] 2. Construct a triangular mesh to generate the initial terrain surface;

[0015] 2.1 Use incremental insertion strategy to build a two-dimensional TIN grid for the filtered discrete elevation data points:

[0016] 1) Build the basic framework of a 2D TIN mesh: Each elevation data point has 3D coordinates (x, y, z). On the xy plane, three non-collinear points are selected from the point set based on their xy coordinates. These three points serve as vertices to form an initial triangle. This initial triangle serves as the basic framework for Delaunay triangulation, providing the topological foundation for subsequent point insertion and mesh expansion.

[0017] 2) Incremental insertion of new points: Dynamically adjust the grid based on the location of the newly added points, specifically:

[0018] External points: connect to the nearest edge / vertex of the convex hull to generate a new triangle;

[0019] Edge point: split the original triangle into two new triangles (single side) or four new triangles (common side);

[0020] Internal point: Split the original triangle into three sub-triangles.

[0021] 2.2 Edge flip method to optimize 2D TIN grid:

[0022] The Delaunay condition requires that the circumcircle of a triangle must not contain other points. Incrementally inserting new points can violate this condition. Using the edge flipping method, we locally adjust the 2D TIN mesh to re-enforce the Delaunay condition. This method modifies only the affected areas, maintaining overall mesh stability while balancing accuracy and efficiency.

[0023] 1) Check the Delaunay condition: After inserting a new point, verify whether the circumcircle of the newly generated triangle contains the other points. If so, such as point D being inside the circumcircle of triangle ABC, the Delaunay condition is violated and needs to be adjusted.

[0024] 2) Identify and adjust problem edges: Determine the affected edge, remove it and replace it with a new edge.

[0025] 3) Recursive verification and repair: Continue to verify the Delaunay condition for the newly generated triangle. If it is still not satisfied, continue the edge flip operation until all adjacent triangles meet the standard.

[0026] 2.3 Assign a corresponding elevation value z to each triangle vertex in the two-dimensional TIN network to form a three-dimensional TIN grid model. By combining spatial position and elevation data, it truly reflects the ups and downs of the terrain.

[0027] 2.4 Traverse all triangles and calculate the ratio of the side length of each triangle. Triangles with too small an area may be caused by uneven distribution of data points or measurement errors and cannot effectively reflect the true characteristics of the terrain. After removing them, triangles that are key to expressing the terrain characteristics can be retained, thus forming the initial terrain surface.

[0028] 3. Use ray lattice sampling technology to optimize the initial terrain surface and obtain the updated terrain;

[0029] The triangular mesh generated by the Delaunay triangulation algorithm for the initial terrain surface is heavily dependent on the initial terrain data. Ray lattice sampling dynamically adjusts ray density based on terrain complexity. In areas with flat or slowly changing terrain, a sparse ray distribution is used to reduce computational effort. In areas with highly undulating terrain or rich features, ray density is increased to improve sampling accuracy and ensure complete capture of terrain details. Therefore, a ray lattice is arranged above the initial terrain surface, with its length, width, and row and column spacing determined based on accuracy and speed requirements. Rays are then emitted in the negative z-axis. The rays emitted by the lattice collide with the initial terrain surface, resulting in the coordinates of the return points. The Delaunay triangulation algorithm is then applied again to these return points to obtain the updated terrain.

[0030] 4. Analyze the inclination and orientation of the terrain to select areas suitable for photovoltaic array layout.

[0031] The design of mountain photovoltaic power stations must take the undulations of the terrain into account. Areas with steep slopes are difficult to erect, require extensive manual leveling, and can also damage the ecological environment. Therefore, these areas are not suitable for mounting. Furthermore, the terrain's azimuth is an important indicator for assessing the suitability of an area for installation. In the Northern Hemisphere, areas facing north are susceptible to shadowing, resulting in poor irradiation and even over- or under-matching of the photovoltaic system. Therefore, it is best to arrange arrays in areas facing south, west, or east. Therefore, before automated deployment of mountain photovoltaic power stations, a comprehensive analysis of the terrain's slope and azimuth is required.

[0032] The specific steps are as follows:

[0033] 4.1 Data input: Select the face to be processed, extract all vertex coordinates, and obtain the coordinate information of each triangle;

[0034] 4.2 Geometric calculation: Calculate the normal vector of the surface by cross-product of the edge vectors, and calculate the inclination and azimuth. The formula for calculating the inclination and azimuth of the terrain surface is as follows:

[0035]

[0036] Where: ∠ inc—inclination angle of the triangle face; τ—normal vector of the triangle face; i—unit vector in the positive direction of the z-axis; ∠ azi —triangle face azimuth; τ hor —The projection of the triangle face on the horizontal plane; i y —Unit vector in the negative direction of the y-axis.

[0037] 4.3 Visualizing the Slope and Azimuth of the Terrain: To clearly convey the relief of the terrain, the inclination and azimuth are presented separately. First, for the inclination analysis process, set the gradient color of the inclination heatmap from green to red, with the minimum inclination corresponding to green and the maximum to red. Select the terrain to be analyzed, obtain the coordinates of the terrain triangulation, calculate the inclination value, store it in an array, and assign a color to each triangle based on the array value. Next, for the azimuth analysis process, the terrain's azimuth determines the amount of irradiance it can receive. For the Northern Hemisphere, for example, the azimuth facing due south provides the best irradiance, while the azimuth facing north provides the worst. Define the azimuth as 0° towards south, -90° towards east, 90° towards west, and ±180° towards north. Azimuths greater than 90° indicate north-facing slopes, colored red; azimuths between 0° and ±90° indicate southeast or southwest-facing slopes, colored blue; and 0° indicates south-facing slopes, colored green. Among them, the inclination range, the color of the maximum inclination value, the azimuth definition and the color of the azimuth can all be adjusted according to your own design requirements.

[0038] 4.4. Comprehensively analyze the inclination and azimuth angles to select areas suitable for photovoltaic array layout.

[0039] Areas suitable for PV panel deployment should be screened based on slope aspect and inclination, taking into account solar radiation resources, construction difficulty, and topographical factors. Specifically, south slopes should have an inclination of less than or equal to 40°, east and west slopes should have an inclination of less than or equal to 20°, and north slopes should have an inclination of less than or equal to 10°. These areas are considered optimal for PV panel deployment and are marked green. Other areas are considered unsuitable for PV panel deployment due to sunlight, construction difficulty, or other factors and are marked red.

[0040] The present invention also provides a terrain modeling and optimization device for a mountain photovoltaic power station, comprising:

[0041] Data filtering module, used to filter the initial data and remove redundant data and noise points;

[0042] Triangular mesh construction module, used to construct triangular mesh and generate initial terrain surface;

[0043] Terrain optimization module, used to optimize the initial terrain surface using ray lattice sampling technology to obtain updated terrain;

[0044] The inclination and orientation analysis module is used to analyze the inclination and orientation of the terrain and select areas suitable for photovoltaic array layout.

[0045] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0046] The beneficial effects of the present invention are:

[0047] The present invention provides a terrain modeling and optimization method, device, and storage medium for mountain photovoltaic power stations, primarily utilizing technologies such as the ray method and the TIN algorithm. By setting the spacing density of the ray array, the total amount of mountain modeling can be reduced and the refinement of the modeling can be locally improved, thereby resolving issues such as long modeling operation time and insufficient accuracy. Furthermore, accurate terrain modeling makes the layout of photovoltaic modules more scientific and reasonable. By analyzing the terrain inclination and orientation for terrain screening, the power generation of mountain photovoltaic power stations can be maximized, and the construction difficulty and cost can be reduced. This includes avoiding complex terrain to simplify construction and optimizing layout to improve land utilization. The specific advantages are as follows:

[0048] 1. Improve modeling efficiency: Through a unified grid generation algorithm, complex terrain can be quickly converted into a structured grid, reducing the tedious steps of manual terrain data processing and optimization workload, and significantly shortening modeling time.

[0049] 2. Enhanced computational stability: The regularly triangulated grid structure has a consistent topological relationship, avoiding the narrow and long triangles that may appear in irregular grids, thereby improving the stability and accuracy of numerical calculations.

[0050] 3. Facilitates dynamic adjustments based on design requirements: Terrains with steep slopes and large elevation differences are not suitable for mountain photovoltaic power plant construction. Therefore, in complex mountainous terrain, high-density meshes can be used in key areas, while sparse meshes can be used in non-construction areas, balancing accuracy and computing resources. This not only reduces the time required to process large-scale mountain data but also ensures that the accuracy requirements of design and simulation are met in areas suitable for mountain photovoltaic power plant construction.

[0051] 4. Improved visualization: Freed from the limitations of single two-dimensional contour lines and CAD drawings, rich and intuitive visualization allows users to more quickly and accurately understand the three-dimensional spatial structure and detailed features of the terrain, greatly improving the efficiency of conveying terrain information.

[0052] 5. Ensuring power station performance: The installation angle and orientation of photovoltaic panels are key factors affecting their power generation efficiency. A suitable installation angle maximizes the amount of solar radiation received by the panels, thereby improving power generation efficiency. In the Northern Hemisphere, aligning the panels to the south maximizes solar radiation. By analyzing the terrain's inclination and orientation to select suitable terrain, the power generation of mountain photovoltaic power stations can be significantly increased. Areas with steep slopes and complex terrain can also be avoided, reducing construction complexity and optimizing the layout of photovoltaic panels. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 is a top view of a contour model of an embodiment;

[0055] Figure 3 is an axonometric view of a contour model of an embodiment;

[0056] Figure 4 The contour model of the embodiment uses the Delaunay triangulation algorithm to generate a triangular mesh, and after retaining valid triangles, a top view of the initial terrain surface model is obtained;

[0057] Figure 5 yes Figure 3 The contour model of the embodiment uses the Delaunay triangulation algorithm to generate a triangular mesh, and after retaining valid triangles, an axonometric view of the initial terrain surface model is obtained;

[0058] Figure 6 is a schematic diagram of a ray lattice established above the contour model of the embodiment;

[0059] Figure 7 is a schematic diagram of the relative positions of the ray dot array established above the contour model of the embodiment;

[0060] Figure 8 Each starting point of the dot matrix emits rays to the ground and records the X, Y, and Z values ​​of each intersection;

[0061] Figure 9 This is a schematic diagram (axonometric view) of the intersection points triangulated using the TIN algorithm;

[0062] Figure 10 This is a schematic diagram of the intersection being triangulated using the TIN algorithm (post-axonometric view);

[0063] Figure 11 is the updated terrain dip heat map;

[0064] Figure 12 It is the slope heat map of the updated terrain;

[0065] Figure 13 It is the recommended layout area for photovoltaic modules;

[0066] Figure 14 It is a schematic diagram for identifying and adjusting problem edges. DETAILED DESCRIPTION

[0067] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0068] like Figure 1 As shown, a terrain modeling and optimization method for a mountain photovoltaic power station includes the following steps:

[0069] 1. Filter the initial data to remove redundant data and noise points;

[0070] Initial data contains a vast number of sampling points, including both key terrain features and a significant amount of redundant data and noise. Redundant data increases the computational complexity of subsequent triangulation and reduces algorithm efficiency. Noise interferes with the accurate representation of terrain features and affects the accuracy of the terrain model. To improve the efficiency and accuracy of the TIN algorithm, this patent uses a 3D line synthesis method to filter the initial data for the TIN algorithm.

[0071] Applying the 3D line synthesis method to the initial data filtering of the TIN algorithm accurately identifies points that play a key role in expressing terrain features. This method removes redundant points that have little impact on the overall terrain form, reducing the data volume, lowering the computational effort involved in TIN construction, and improving algorithm speed. Furthermore, it filters out noise points, preventing them from interfering with the terrain model, ensuring that the constructed TIN model more accurately reflects the actual terrain features, thereby improving terrain modeling accuracy and reliability.

[0072] 2. Construct a triangular mesh to generate the initial terrain surface;

[0073] 2.1. Use incremental insertion strategy to build a 2D TIN grid for the filtered discrete elevation data points:

[0074] 1) Build the basic framework of a 2D TIN mesh: Each elevation data point has 3D coordinates (x, y, z). On the xy plane, three non-collinear points are selected from the point set based on their xy coordinates. These three points serve as vertices to form an initial triangle. This initial triangle serves as the basic framework for Delaunay triangulation, providing the topological foundation for subsequent point insertion and mesh expansion.

[0075] 2) Incremental insertion of new points: Dynamically adjust the grid based on the location of the newly added points, specifically:

[0076] External points: connect to the nearest edge / vertex of the convex hull to generate a new triangle;

[0077] Edge point: split the original triangle into two new triangles (single side) or four new triangles (common side);

[0078] Internal point: Split the original triangle into three sub-triangles.

[0079] 2.2. Edge flip method to optimize 2D TIN grid:

[0080] The Delaunay condition requires that the circumcircle of a triangle must not contain other points. Incrementally inserting new points can violate this condition. Using the edge flipping method, we locally adjust the 2D TIN mesh to re-enforce the Delaunay condition. This method modifies only the affected areas, maintaining overall mesh stability while balancing accuracy and efficiency.

[0081] 1) Check the Delaunay condition: After inserting a new point, verify whether the circumcircle of the newly generated triangle contains the other points. If so, such as point D being inside the circumcircle of triangle ABC, the Delaunay condition is violated and needs to be adjusted.

[0082] 2) Identify and adjust problem edges: Identify the affected edges, remove them and replace them with new edges. Figure 14 As shown, if point D is inside the circumcircle of triangle ABC and is associated with side BC, then side BC is removed and replaced by AD, forming new triangles ACD and ABD, thus satisfying the Delaunay condition.

[0083] 3) Recursive verification and repair: Continue to verify the Delaunay condition for the newly generated triangle. If it is still not satisfied, continue the edge flip operation until all adjacent triangles meet the standard.

[0084] 2.3. Assign a corresponding elevation value z to each triangle vertex in the two-dimensional TIN network to form a three-dimensional TIN grid model. By combining spatial position and elevation data, it truly reflects the ups and downs of the terrain.

[0085] 2.4. Traverse all triangles and calculate the ratio of the side lengths of each triangle. Triangles with too small an area may be caused by uneven distribution of data points or measurement errors and cannot effectively reflect the true characteristics of the terrain. After removing them, triangles that are key to expressing the terrain characteristics can be retained, thus forming the initial terrain surface.

[0086] 3. Use ray lattice sampling technology to optimize the initial terrain surface and obtain the updated terrain;

[0087] The triangular mesh generated by the Delaunay triangulation algorithm for the initial terrain surface is heavily dependent on the initial terrain data. Ray lattice sampling dynamically adjusts ray density based on terrain complexity. In areas with flat or slowly changing terrain, a sparse ray distribution is used to reduce computational effort. In areas with highly undulating terrain or rich features, ray density is increased to improve sampling accuracy and ensure complete capture of terrain details. Therefore, a ray lattice is arranged above the initial terrain surface, with its length, width, and row and column spacing determined based on accuracy and speed requirements. Rays are then emitted in the negative z-axis. The rays emitted by the lattice collide with the initial terrain surface, resulting in the coordinates of the return points. The Delaunay triangulation algorithm is then applied again to these return points to obtain the updated terrain.

[0088] 4. Analyze the inclination and orientation of the terrain to select areas suitable for photovoltaic array layout.

[0089] The design of mountain photovoltaic power stations must take the undulations of the terrain into account. Areas with steep slopes are difficult to erect, require extensive manual leveling, and can also damage the ecological environment. Therefore, these areas are not suitable for mounting. Furthermore, the terrain's azimuth is an important indicator for assessing the suitability of an area for installation. In the Northern Hemisphere, areas facing north are susceptible to shadowing, resulting in poor irradiation and even over- or under-matching of the photovoltaic system. Therefore, it is best to arrange arrays in areas facing south, west, or east. Therefore, before automated deployment of mountain photovoltaic power stations, a comprehensive analysis of the terrain's slope and azimuth is required.

[0090] The specific steps are as follows:

[0091] 4.1 Data input: Select the face to be processed, extract all vertex coordinates, and obtain the coordinate information of each triangle;

[0092] 4.2 Geometric calculation: Calculate the normal vector of the surface by cross-product of the edge vectors, and calculate the inclination and azimuth. The formula for calculating the inclination and azimuth of the terrain surface is as follows:

[0093]

[0094] Where: ∠ inc —inclination angle of the triangle face; τ—normal vector of the triangle face; i—unit vector in the positive direction of the z-axis; ∠ azi —triangle face azimuth; τ hor —The projection of the triangle face on the horizontal plane; i y —Unit vector in the negative direction of the y-axis.

[0095] 4.3 Visualizing the Slope and Azimuth of the Terrain: To clearly convey the relief of the terrain, the inclination and azimuth are presented separately. First, for the inclination analysis process, set the gradient color of the inclination heatmap from green to red, with the minimum inclination corresponding to green and the maximum to red. Select the terrain to be analyzed, obtain the coordinates of the terrain triangulation, calculate the inclination value, store it in an array, and assign a color to each triangle based on the array value. Next, for the azimuth analysis process, the terrain's azimuth determines the amount of irradiance it can receive. For the Northern Hemisphere, for example, the azimuth facing due south provides the best irradiance, while the azimuth facing north provides the worst. Define the azimuth as 0° towards south, -90° towards east, 90° towards west, and ±180° towards north. Azimuths greater than 90° indicate north-facing slopes, colored red; azimuths between 0° and ±90° indicate southeast or southwest-facing slopes, colored blue; and 0° indicates south-facing slopes, colored green. Among them, the inclination range, the color of the maximum inclination value, the azimuth definition and the color of the azimuth can all be adjusted according to your own design requirements.

[0096] 4.4. Comprehensively analyze the inclination and azimuth angles to select areas suitable for photovoltaic array layout.

[0097] Areas suitable for PV panel deployment should be screened based on slope aspect and inclination, taking into account solar radiation resources, construction difficulty, and topographical factors. Specifically, south slopes should have an inclination of less than or equal to 40°, east and west slopes should have an inclination of less than or equal to 20°, and north slopes should have an inclination of less than or equal to 10°. These areas are considered optimal for PV panel deployment and are marked green. Other areas are considered unsuitable for PV panel deployment due to sunlight, construction difficulty, or other factors and are marked red.

[0098] The present invention also provides a terrain modeling and optimization device for a mountain photovoltaic power station, comprising:

[0099] The data filtering module is used to filter the initial data and remove redundant data and noise points; the triangular mesh construction module is used to construct the triangular mesh and generate the initial terrain surface;

[0100] Terrain optimization module, used to optimize the initial terrain surface using ray lattice sampling technology to obtain updated terrain;

[0101] The inclination and orientation analysis module is used to analyze the inclination and orientation of the terrain and select areas suitable for photovoltaic array layout.

[0102] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0103] Example:

[0104] This example is a visualization display effect, and Sketchup software is used for simulation. The present invention does not need to rely on Sketchup for use.

[0105] Step 1: Filter the initial data

[0106] Import the contour line model of a mountainous area in Zhenjiang, such as Figure 2 and Figure 3 Its geographical location is: 119.1150°E, 32.0750°N. The model is approximately 502m long and 450m wide, covering an area of ​​approximately 226,000 square meters, with a maximum elevation of 400m.

[0107] Contour lines are composed of a series of continuous broken lines formed by connecting straight line segments end to end, with adjacent line segments sharing vertices. However, due to factors such as diverse data sources and shared vertices between adjacent line segments, duplicate points often appear between connected line segments. Duplicate points can cause the display of graphics to appear uneven and jagged. During initialization, the starting and end points of the curve are selected as the initial feature points. Then, recursive segmentation is performed to calculate the perpendicular distance from each point on the curve to the current line segment and find the one with the largest distance. If the distance exceeds the threshold, this point is used as a new feature point and the curve is split into two segments. The above operation is then repeated for each curve segment until the perpendicular distance from all points to the corresponding line segment is less than the threshold. Finally, all feature points are retained to obtain a simplified curve or surface.

[0108] Traversing all entities in the model space, a total of 1047 endpoints are obtained after removing duplicate points.

[0109] Step 2: Construct a triangular mesh

[0110] The Delaunay triangulation algorithm is used to generate a triangular mesh for the endpoints read in the first step, and the initial terrain surface is obtained after retaining the valid triangles, such as Figure 4 and Figure 5 shown.

[0111] Step 3: Terrain Optimization

[0112] Based on the model information and considering the algorithm accuracy and running speed, a matrix with a length of 600m, a width of 540m, and a row and column spacing of 30m is established 50m above the model. Assuming that one of the areas is planned to be a mountain photovoltaic power station area, it requires detailed modeling. To achieve a better model display effect, a matrix with a length of 180m, a width of 180m, and a row and column spacing of 12m is established above the area. The matrix has a total of 567 ray starting points, such as Figure 6-7 shown.

[0113] Each starting point emits rays to the ground. A total of 464 rays hit the terrain, and 103 rays miss. The X, Y, and Z values ​​of each intersection are recorded, and the return point is as follows: Figure 8 shown.

[0114] These intersections are triangulated using the TIN algorithm. The TIN algorithm forms an irregular triangular mesh by connecting these points. After generating the triangular mesh, a Z value (height) is added to each intersection based on the actual terrain data. The generated terrain is as follows Figure 9 and Figure 10 shown.

[0115] Step 4: Analysis of inclination and orientation

[0116] The inclination heat map can more accurately display the inclination of each terrain surface by directly mapping the inclination value, helping to screen out areas that meet specific inclination requirements. The heat map will color the terrain surface according to the inclination value. The inclination value and its color setting can be changed according to your own design requirements. This example sets the inclination value as follows: the surface with an inclination less than 5° is set to green, indicating that it is relatively flat; the surface with an inclination between 5 and 10 is set to yellow-green; the surface between 10 and 15° is yellow; the surface between 15° and 20° is orange; the surface with an inclination greater than 20° is set to red, indicating that it is relatively steep. The display effect is as follows Figure 11 shown.

[0117] The terrain surface can be divided into four directions according to the slope direction: east, south, west, and north. The photovoltaic resources on the south slope are the richest, followed by the west slope, the east slope is weaker, and the north slope is the worst. The south slope receives the most sunlight, and the west slope has the strongest sunlight in the afternoon: the east slope has stronger sunlight in the morning and weaker sunlight in the afternoon, while the north slope has the shortest sunlight exposure time and the poorest resources. The slope direction can be judged by the azimuth, with the south being 0, the east direction being negative, and the west direction being positive. According to the range of the azimuth, the slope direction can be classified as follows: the south slope (-45° to 45°) has the richest radiation resources and is red; the west slope (45° to 135°) has strong sunlight in the afternoon and is assigned orange-yellow; the east slope (-45° to -135°) has strong sunlight in the morning and is assigned orange-yellow; the north slope (135° to 180° or -180° to -135°) has the worst radiation resources and is assigned yellow. The slope heat map display effect is as follows: Figure 12 shown.

[0118] Comprehensively analyze the inclination and azimuth to filter the terrain, add color, and the green display part can be arranged. Figure 13 shown.

[0119] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A terrain modeling and optimization method for a mountain photovoltaic power station, characterized in that: The following steps are involved: 1.

1. Filter the initial data to remove redundant data and noise points; the initial data filtering step uses the initial data of the 3D line synthesis method TIN algorithm to filter; 1.

2. Construct a triangular mesh to generate the initial terrain surface; 1.

3. Use ray lattice sampling technology to optimize the initial terrain surface and obtain the updated terrain; 1.

4. Analyze the inclination and orientation of the terrain to select areas suitable for photovoltaic array layout.

2. A mountain photovoltaic power station terrain modeling and optimization method according to claim 1, characterized in that: The step of constructing a triangular mesh comprises: 2.

1. Use incremental insertion strategy to build a 2D TIN grid for the filtered discrete elevation data points: 2.1.1) Constructing the basic framework of a 2D TIN grid: Each elevation data point has 3D coordinates (x, y, z). On the xy plane, three non-collinear points are selected from the point set based on their xy coordinates. These three points serve as vertices to form an initial triangle. This initial triangle serves as the basic framework for Delaunay triangulation, providing the topological foundation for subsequent point insertion and grid expansion. 2.1.2) Incremental insertion of new points: Dynamically adjust the grid based on the location of the newly added points, specifically: External points: connect to the nearest edge / vertex of the convex hull to generate a new triangle; Edge points: split the original triangle into two new triangles or four new triangles; Internal point: split the original triangle into three sub-triangles; 2.

2. Edge flip method to optimize 2D TIN grid: The Delaunay condition requires that the triangle circumcircle must not contain other points. Incremental insertion of new points may violate the Delaunay condition. The edge flip method is used to locally adjust the 2D TIN mesh so that the structure can satisfy the Delaunay condition again. 2.2.1) Check the Delaunay condition: After inserting a new point, verify whether the circumcircle of the newly generated triangle contains other points. If so, such as point D being inside the circumcircle of triangle ABC, the Delaunay condition is violated and needs to be adjusted. 2.2.2) Identify and adjust problem edges: Identify the affected edges, remove them and replace them with new ones; 2.23) Recursive verification and repair: Continue to verify the Delaunay condition for the newly generated triangle. If it still does not meet the criteria, continue the edge flip operation until all adjacent triangles meet the criteria; 2.

3. Assign a corresponding elevation value z to each triangle vertex in the two-dimensional TIN network to form a three-dimensional TIN grid model. By combining spatial position and elevation data, it can truly reflect the ups and downs of the terrain. 2.

4. Traverse all triangles and calculate the ratio of the side lengths of each triangle. Triangles with too small an area may be caused by uneven distribution of data points or measurement errors and cannot effectively reflect the true characteristics of the terrain. After removing them, triangles that are key to expressing the terrain characteristics can be retained, thus forming the initial terrain surface.

3. A mountain photovoltaic power station terrain modeling and optimization method according to claim 1, characterized in that: The terrain optimization step comprises: 3.

1. Arrange a ray array above the initial terrain surface and dynamically adjust the ray density according to the complexity of the terrain; 3.

2. Emit a ray in the negative direction of the z-axis, collide with the initial terrain surface, and obtain the coordinates of the return point; 3.

3. Use the Delaunay triangulation algorithm again on the returned points to obtain the updated terrain.

4. A mountain photovoltaic power station terrain modeling and optimization method according to claim 1, characterized in that: The inclination and orientation analysis step includes: 4.

1. Data input: Select the face to be processed, extract all vertex coordinates, and obtain the coordinate information of each triangle; 4.

2. Geometric calculation: Calculate the normal vector of the surface by cross-product of the edge vectors, and calculate the inclination and azimuth. The formula for calculating the inclination and azimuth of the terrain surface is as follows: Where: ∠ inc —inclination angle of the triangle face; τ—normal vector of the triangle face; i—unit vector in the positive direction of the z-axis; ∠ azi —triangle face azimuth; τ hor —The projection of the triangle face on the horizontal plane; i y —unit vector in the negative direction of the y-axis; 4.

3. Visual display of terrain slope and azimuth; 4.

4. Comprehensively analyze the inclination and azimuth angles to select areas suitable for photovoltaic array layout.

5. A terrain modeling and optimization device for a mountain photovoltaic power station, characterized in that: include: Data filtering module, used to filter the initial data and remove redundant data and noise points; Triangular mesh construction module, used to construct triangular mesh and generate initial terrain surface; Terrain optimization module, used to optimize the initial terrain surface using ray lattice sampling technology to obtain updated terrain; The inclination and orientation analysis module is used to analyze the inclination and orientation of the terrain and select areas suitable for photovoltaic array layout.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images